Projects
Delay Discounting as a Latent Factor
Date: December 15, 2024
One Factor Model:
CFI = .72
RMSEA = .24
SRMR = .109
Avg R2 = .60
Two Factor Model:
CFI = .94
RMSEA = .12
SRMR = .04
Avg R2 = .69
Four Factor Model:
CFI = .96
RMSEA = .10
SRMR = .04
Avg R2 = .69
Which ML Algorithms Predict Job Satisfaction The Best?
Date: May 2, 2023
Machine learning algorithms have gained significant popularity in I/O psychology due to their advanced learning capabilities, often outperforming traditional regression methods in predictive tasks. However, their “black-box” nature remains a challenge for research justification. This project compares the performance of baseline model logistic regression with popular algorithms KNN, and random forest in a 4-class job satisfaction classification task using the IBM HR dataset from Kaggle, comprising approximately 23,000 observations. Using lasso-based feature-selection methods, hyperparameter tuning, the project optimizes model performance and identifies the algorithm with the highest predictive accuracy. The findings offer actionable insights into employee well-being, showcasing the potential of data-driven approaches to enhance workforce engagement and organizational performance.